scholarly journals Bayesian calibration of a k-ε turbulence model for predictive jet-in-crossflow simulations

2014 ◽  
Author(s):  
Jaideep Ray ◽  
Sophia Lefantzi ◽  
Srinivasan Arunajatesan ◽  
Lawrence J. DeChant
Keyword(s):  
Turbulence Model ◽  
Bayesian Calibration ◽  
Jet In Crossflow

scholarly journals Robust Bayesian Calibration of a RANS Model for Jet-in-Crossflow Simulations

2017 ◽  
Author(s):  
Jaideep Ray ◽  
Sophia Lefantzi ◽  
Srinivasan Arunajatesan ◽  
Lawrence J. DeChant
Keyword(s):  
Bayesian Calibration ◽  
Rans Model ◽  
Jet In Crossflow

scholarly journals Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations

AIAA Journal ◽  
2018 ◽  
Vol 56 (12) ◽  
pp. 4893-4909 ◽  
Author(s):  
Jaideep Ray ◽  
Lawrence Dechant ◽  
Sophia Lefantzi ◽  
Julia Ling ◽  
Srinivasan Arunajatesan
Keyword(s):  
Bayesian Calibration ◽  
Jet In Crossflow ◽  
Compressible Jet

scholarly journals The Recirculation Zone Characteristics of the Circular Transverse Jet in Crossflow

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3224
Author(s):  
Ziwan Li ◽  
Yixiang Yuan ◽  
Baoting Guo ◽  
V. L. Varsegov ◽  
Jun Yao
Keyword(s):  
Turbulence Model ◽  
Negative Pressure ◽  
Recirculation Zone ◽  
Turbulence Models ◽  
Combustion Efficiency ◽  
Width Ratio ◽  
Transverse Jet ◽  
Jet In Crossflow ◽  
Pressure Zone ◽  
Jet Trajectory

Transverse jets in crossflow are widely used in energy systems, especially as dilution air jets, fuel/air mixers, and combustion equipment, and have received extensive attention and plenty of research. However, the studies of the circular transverse jet issued from a circular gap at the circumferential direction of a tube in crossflow are very limited. This paper studies a relatively new jet: the circular transverse jet. Firstly, numerical calculations are conducted under different turbulence models but with the same boundary conditions. By comparing the numerical results of different turbulence models with the existing experimental data, the turbulence model which is most suitable for the numerical calculation of the circular transverse jet is selected. Then, this turbulence model is used to calculate and analyze the flow field structure and its characteristics. It is found that due to the aerodynamic barrier effect of the high-velocity jet, a negative pressure zone is formed behind the jet trajectory; the existence of the negative pressure zone causes the formation of a vortex structure and a recirculation zone downstream the circular transverse jet; and the length/width ratio of the recirculation zone does not change with the changes of the crossflow and the jet parameters. It means that the recirculation zone is a fixed shape for a definite device. This would be fundamental references for the studying of fuel/air mixing characteristics and combustion efficiency when the circular transverse jet is used as a fuel/air mixer and stable combustion system.

Computational Study of Jet-in-Crossflow and Film Cooling Using a New Unsteady-Based Turbulence Model

2005 ◽  
Author(s):  
D. Scott Holloway ◽  
D. Keith Walters ◽  
James H. Leylek
Keyword(s):  
Turbulence Model ◽  
Film Cooling ◽  
Computational Study ◽  
Velocity Ratio ◽  
Density Ratio ◽  
Turbulence Models ◽  
Streamwise Velocity ◽  
Pressure Side ◽  
Good Test ◽  
Jet In Crossflow

This paper documents a computational investigation of the unsteady behavior of jet-in-crossflow applications. Improved prediction of fundamental physics is achieved by implementing a new unsteady, RANS-based turbulence model developed by the authors. Two test cases are examined that match experimental efforts previously documented in the open literature. One is the well-documented normal jet-in-crossflow, and the other is film cooling on the pressure side of a turbine blade. All simulations are three-dimensional, fully converged, and grid-independent. High-quality and high-density grids are constructed using multiple topologies and an unstructured, super-block approach to ensure that numerical viscosity is minimized. Computational domains include the passage, film hole, and coolant supply plenum. Results for the normal jet-in-crossflow are for a density ratio of 1 and velocity ratio of 0.5 and include streamwise velocity profiles and injected flow or “coolant” distribution. The Reynolds number based on the average jet exit velocity and jet diameter is 20,500. This represents a good test case since normal injection is known to exaggerate the key flow mechanisms seen in film-cooling applications. Results for the pressure side film-cooling case include coolant distribution and adiabatic effectiveness for a density and blowing ratio of 2. In addition to the in-house model that incorporates new unsteady physics, CFD simulations utilize standard, RANS-based turbulence models, such as the “realizable” k-ε model. The present study demonstrates the importance of unsteady physics in the prediction of jet-in-crossflow interactions and for film cooling flows that exhibit jet liftoff.

scholarly journals The Effect of Schmidt Number on Turbulent Scalar Mixing in a Jet-in-Crossflow

1999 ◽  
Author(s):  
Guangbin He ◽  
Yanhu Guo ◽  
Andrew T. Hsu ◽  
A. Brankovic ◽  
S. Syed ◽  
...  
Keyword(s):  
Turbulence Model ◽  
Momentum Flux ◽  
Schmidt Number ◽  
Stokes Equations ◽  
Scalar Fields ◽  
Spreading Rate ◽  
Navier Stokes ◽  
Appreciable Effect ◽  
Jet In Crossflow ◽  
Schmidt Numbers

The adequacy and accuracy of the constant Schmidt number assumption in predicting turbulent scalar fields in jet-in-crossflows are assessed in the present work. A round jet injected into a confined crossflow in a rectangular tunnel has been simulated using the Reynolds-Averaged Navier-Stokes equations coupled with the standard k-ε turbulence model. A semi-analytical qualitative analysis was made to guide the selection of Schmidt number values. A series of parametric studies were performed, and Schmidt numbers ranging from 0.2 to 1.5 and jet-to-crossflow momentum flux ratios from 8 to 72 were tested. The principal observation is that the Schmidt number does not have an appreciable effect on the species penetration, but it does have a significant effect on species spreading rate in jet-in-crossflows, especially for the cases where the jet-to-crossflow momentum flux ratios are relatively small. A Schmidt number of 0.2 is recommended for best agreement with data. The limitations of the standard k–ε turbulence model and the constant Schmidt number assumption are discussed.

Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

2021 ◽  
Author(s):  
Ruben Bruno Diaz ◽  
Fabiola Paula Costa ◽  
Pedro M. Milani ◽  
Jesuino Takachi Tomita ◽  
Cleverson Bringhenti
Keyword(s):  
Machine Learning ◽  
Turbulence Model ◽  
Transverse Jet ◽  
Jet In Crossflow
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